The generational scalability of single-cell replicative aging

Transcriptional drift in aging cells: A global decontroller

As cells age, they undergo a remarkable global change: In transcriptional drift, hundreds of genes become overexpressed while hundreds of others become underexpressed. Using archetype modeling and Gene Ontology analysis on data from aging Caenorhabditis elegans worms, we find that the up-regulated genes code for sensory proteins upstream of stress responses and down-regulated genes are growth- and metabolism-related. We observe similar trends within human fibroblasts, suggesting that this process is conserved in higher organisms. We propose a simple mechanistic model for how such global coordination of multiprotein expression levels may be achieved by the binding of a single factor that concentrates with age in C. elegans. A key implication is that a cell's own responses are part of its aging process, so unlike wear-and-tear processes, intervention might be able to modulate these effects.

Sis2 regulates yeast replicative lifespan in a dose-dependent manner

Application of microfluidic platforms facilitated high-precision measurements of yeast replicative lifespan (RLS); however, comparative quantification of lifespan across strain libraries has been missing. Here we microfluidically measure the RLS of 307 yeast strains, each deleted for a single gene. Despite previous reports of extended lifespan in these strains, we found that 56% of them did not actually live longer than the wild-type; while the remaining 44% showed extended lifespans, the degree of extension was often different from what was previously reported. Deletion of SIS2 gene led to the largest RLS increase observed. Sis2 regulated yeast lifespan in a dose-dependent manner, implying a role for the coenzyme A biosynthesis pathway in lifespan regulation. Introduction of the human PPCDC gene in the sis2Δ background neutralized the lifespan extension. RNA-seq experiments revealed transcriptional increases in cell-cycle machinery components in sis2Δ background. High-precision lifespan measurement will be essential to elucidate the gene network governing lifespan.

Quantification methods of determining brewer's and pharmaceutical yeast cell viability: accuracy and impact of nanoparticles

For industrial processes, a fast, precise, and reliable method of determining the physiological state of yeast cells, especially viability, is essential. However, an increasing number of processes use magnetic nanoparticles (MNPs) for yeast cell manipulation, but their impact on yeast cell viability and the assay itself is unclear. This study tested the viability of Saccharomyces pastorianus ssp. carlsbergensis and Pichia pastoris by comparing traditional colourimetric, high-throughput, and growth assays with membrane fluidity. Results showed that methylene blue staining is only reliable for S. pastorianus cells with good viability, being erroneous in low viability (R² = 0.945; [Formula: see text] = 5.78%). In comparison, the fluorescence microscopy-based assay of S. pastorianus demonstrated a coefficient of determination of R² = 0.991 at [Formula: see text] ([Formula: see text] = 2.50%) and flow cytometric viability determination using carboxyfluorescein diacetate (CFDA), enabling high-throughput analysis of representative cell numbers; R² = 0.972 ([Formula: see text]; [Formula: see text] = 3.89%). Membrane fluidity resulted in a non-linear relationship with the viability of the yeast cells ([Formula: see text]). We also determined similar results using P. pastoris yeast. In addition, we demonstrated that MNPs affected methylene blue staining by overestimating viability. The random forest model has been shown to be a precise method for classifying nanoparticles and yeast cells and viability differentiation in flow cytometry by using CFDA. Moreover, CFDA and membrane fluidity revealed precise results for both yeasts, also in the presence of nanoparticles, enabling fast and reliable determination of viability in many experiments using MNPs for yeast cell manipulation or separation.

The choice of the objective function in flux balance analysis is crucial for predicting replicative lifespans in yeast

Flux balance analysis (FBA) is a powerful tool to study genome-scale models of the cellular metabolism, based on finding the optimal flux distributions over the network. While the objective function is crucial for the outcome, its choice, even though motivated by evolutionary arguments, has not been directly connected to related measures. Here, we used an available multi-scale mathematical model of yeast replicative ageing, integrating cellular metabolism, nutrient sensing and damage accumulation, to systematically test the effect of commonly used objective functions on features of replicative ageing in budding yeast, such as the number of cell divisions and the corresponding time between divisions. The simulations confirmed that assuming maximal growth is essential for reaching realistic lifespans. The usage of the parsimonious solution or the additional maximisation of a growth-independent energy cost can improve lifespan predictions, explained by either increased respiratory activity using resources otherwise allocated to cellular growth or by enhancing antioxidative activity, specifically in early life. Our work provides a new perspective on choosing the objective function in FBA by connecting it to replicative ageing.

Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing

The accumulation of protein damage is one of the major drivers of replicative ageing, describing a cell's reduced ability to reproduce over time even under optimal conditions. Reactive oxygen and nitrogen species are precursors of protein damage and therefore tightly linked to ageing. At the same time, they are an inevitable by-product of the cell's metabolism. Cells are able to sense high levels of reactive oxygen and nitrogen species and can subsequently adapt their metabolism through gene regulation to slow down damage accumulation. However, the older or damaged a cell is the less flexibility it has to allocate enzymes across the metabolic network, forcing further adaptions in the metabolism. To investigate changes in the metabolism during replicative ageing, we developed an multi-scale mathematical model using budding yeast as a model organism. The model consists of three interconnected modules: a Boolean model of the signalling network, an enzyme-constrained flux balance model of the central carbon metabolism and a dynamic model of growth and protein damage accumulation with discrete cell divisions. The model can explain known features of replicative ageing, like average lifespan and increase in generation time during successive division, in yeast wildtype cells by a decreasing pool of functional enzymes and an increasing energy demand for maintenance. We further used the model to identify three consecutive metabolic phases, that a cell can undergo during its life, and their influence on the replicative potential, and proposed an intervention span for lifespan control.

Modeling aging and its impact on cellular function and organismal behavior

Aging is a complex phenomenon of functional decay in a biological organism. Although the effects of aging are readily recognizable in a wide range of organisms, the cause(s) of aging are ill defined and poorly understood. Experimental methods on model organisms have driven significant insight into aging as a process, but have not provided a complete model of aging. Computational biology offers a unique opportunity to resolve this gap in our knowledge by generating extensive and testable models that can help us understand the fundamental nature of aging, identify the presence and characteristics of unaccounted aging factor(s), demonstrate the mechanics of particular factor(s) in driving aging, and understand the secondary effects of aging on biological function. In this review, we will address each of the above roles for computational biology in aging research. Concurrently, we will explore the different applications of computational biology to aging in single-celled versus multicellular organisms. Given the long history of computational biogerontological research on lower eukaryotes, we emphasize the key future goals of gradually integrating prior models into a holistic map of aging and translating successful models to higher-complexity organisms.

Senolytics and the compression of late-life mortality

Senescent cells play an important role in mammalian ageing and in the etiology of age-related diseases. Treatment of mice with senolytics - drugs that selectively remove senescent cells - causes an extension of median lifespan but has little effect on maximum lifespan. Postponement of some mortality to later ages, without a corresponding increase in maximum mortality, can be termed 'compression of mortality'. When we fit the standard Gompertz mortality model to the survival data following senolytic treatment, we find an increase in the slope parameter, commonly described as the 'actuarial ageing rate'. These observations raise important questions about the actions of senolytic treatments and their effects on health and survival, which are not yet sufficiently understood. To explore how the survival data from senolytics experiments might be explained, we combine a recent exploration of the evolutionary basis of cellular senescence with theoretical consideration of the molecular processes that might be involved. We perform numerical simulations of senescent cell accumulation and senolytic treatment in an ageing population. The simulations suggest that while senolytics diminish the burden of senescent cells, they may also impair the general repair capacity of the organism, leading to a faster accumulation post-treatment of new senescent cells. Our results suggest a framework to address the benefits and possible side effects of senolytic therapies, with the potential to aid in the design of optimal treatment regimens.

Common features of aging fail to occur in Drosophila raised without a bacterial microbiome

Lifespan is limited both by intrinsic decline in vigor with age and by accumulation of external insults. There exists a general picture of the deficits of aging, one that is reflected in a pattern of age-correlated changes in gene expression conserved across species. Here, however, by comparing gene expression profiling of Drosophila raised either conventionally, or free of bacteria, we show that ∼70% of these conserved, age-associated changes in gene expression fail to occur in germ-free flies. Among the processes that fail to show time-dependent change under germ-free conditions are two aging features that are observed across phylogeny, declining expression of stress response genes and increasing expression of innate immune genes. These comprise adaptive strategies the organism uses to respond to bacteria, rather than being inevitable components of age-dependent decline. Changes in other processes are independent of the microbiome and can serve as autonomous markers of aging of the individual.

Linking the maximum reported life span to the aging rate in wild birds

Dozens of surrogates have been used to reflect the rate of aging in comparative biology. For wild organisms, the maximum reported life span is often considered a key metric. However, the connection between the maximum reported life span for a single individual and the aging rate of that species is far from clear. Our objective was to identify a pragmatic solution to calculate the aging rate from the maximum reported life span of wild birds. We explicitly linked the maximum reported life span to the aging process by employing a Weibull distribution and calculating the shape parameter in this model, which reflects the change in mortality across ages and be used as a surrogate for the aging rate. From simulated data, we demonstrated that the percentile estimator is suitable for calculating the aging rate based on the maximum reported life span. We also calculated the aging rate in 246 bird species based on published information from EURING and tested its relationship with body mass. Our study constitutes a new approach for using maximum reported life span in aging research. The aging rate calculated in the study is based on numerous assumptions/prerequisites and can be improved as more is learned about these assumptions/prerequisites.

Replicative Aging in Pathogenic Fungi

Candida albicans, Candida auris, Candida glabrata, and Cryptococcus neoformans are pathogenic yeasts which can cause systemic infections in immune-compromised as well as immune-competent individuals. These yeasts undergo replicative aging analogous to a process first described in the nonpathogenic yeast Saccharomyces cerevisiae. The hallmark of replicative aging is the asymmetric cell division of mother yeast cells that leads to the production of a phenotypically distinct daughter cell. Several techniques to study aging that have been pioneered in S. cerevisiae have been adapted to study aging in other pathogenic yeasts. The studies indicate that aging is relevant for virulence in pathogenic fungi. As the mother yeast cell progressively ages, every ensuing asymmetric cell division leads to striking phenotypic changes, which results in increased antifungal and antiphagocytic resistance. This review summarizes the various techniques that are used to study replicative aging in pathogenic fungi along with their limitations. Additionally, the review summarizes some key phenotypic variations that have been identified and are associated with changes in virulence or resistance and thus promote persistence of older cells.

Identifying longevity associated genes by integrating gene expression and curated annotations

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are best for optimizing classification performance and which algorithms are best suited to this task. Further, performance assessments based on held-out test data are lacking. We systematically compare five popular classification algorithms using gene ontology and gene expression datasets as features to predict the pro-longevity versus anti-longevity status of genes for two model organisms (C. elegans and S. cerevisiae) using the GenAge database as ground truth. We find that elastic net penalized logistic regression performs particularly well at this task. Using elastic net, we make novel predictions of pro- and anti-longevity genes that are not currently in the GenAge database.

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are best for optimizing classification performance and which algorithms are best suited to this task. Further, performance assessments based on held-out test data are lacking. We systematically compare five popular classification algorithms using gene ontology and gene expression datasets as features to predict the pro-longevity versus anti-longevity status of genes for two model organisms (nematode worms and yeast) using the GenAge database as ground truth. We find that elastic net penalized logistic regression performs particularly well at this task. Using elastic net, we make novel predictions of pro- and anti-longevity genes that are not currently in the GenAge database.

The synergy of damage repair and retention promotes rejuvenation and prolongs healthy lifespans in cell lineages

Damaged proteins are inherited asymmetrically during cell division in the yeast Saccharomyces cerevisiae, such that most damage is retained within the mother cell. The consequence is an ageing mother and a rejuvenated daughter cell with full replicative potential. Daughters of old and damaged mothers are however born with increasing levels of damage resulting in lowered replicative lifespans. Remarkably, these prematurely old daughters can give rise to rejuvenated cells with low damage levels and recovered lifespans, called second-degree rejuvenation. We aimed to investigate how damage repair and retention together can promote rejuvenation and at the same time ensure low damage levels in mother cells, reflected in longer health spans. We developed a dynamic model for damage accumulation over successive divisions in individual cells as part of a dynamically growing cell lineage. With detailed knowledge about single-cell dynamics and relationships between all cells in the lineage, we can infer how individual damage repair and retention strategies affect the propagation of damage in the population. We show that damage retention lowers damage levels in the population by reducing the variability across the lineage, and results in larger population sizes. Repairing damage efficiently in early life, as opposed to investing in repair when damage has already accumulated, counteracts accelerated ageing caused by damage retention. It prolongs the health span of individual cells which are moreover less prone to stress. In combination, damage retention and early investment in repair are beneficial for healthy ageing in yeast cell populations.

Quantitative Insights into Age-Associated DNA-Repair Inefficiency in Single Cells

Although double-strand break (DSB) repair is essential for a cell's survival, little is known about how DSB repair mechanisms are affected by age. Here we characterize the impact of cellular aging on the efficiency of single-strand annealing (SSA), a DSB repair mechanism. We measure SSA repair efficiency in young and old yeast cells and report a 23.4% decline in repair efficiency. This decline is not due to increased use of non-homologous end joining. Instead, we identify increased G1 phase duration in old cells as a factor responsible for the decreased SSA repair efficiency. Expression of 3xCLN2 leads to higher SSA repair efficiency in old cells compared with expression of 1xCLN2, confirming the involvement of cell-cycle regulation in age-associated repair inefficiency. Examining how SSA repair efficiency is affected by sequence heterology, we find that heteroduplex rejection remains high in old cells. Our work provides insights into the links between single-cell aging and DSB repair efficiency.

Characterization of the impact of GMP/GDP synthesis inhibition on replicative lifespan extension in yeast

Slowing down aging-associated accumulation of molecular damage or its prevention represents a promising therapeutic paradigm to combat aging-related disease and death. While several chemical compounds extend lifespan in model organisms, their mechanism of action is often unknown, reducing their therapeutic potential. Using a systematic approach, here we characterize the impact of the GMP pathway on yeast lifespan and elucidate GMP synthesis inhibition as a lifespan extension mechanism. We further discover that proteasome activation extends lifespan in part through the GMP pathway. GMP synthesis inhibition exerts its lifespan extension effect independently of the canonical nutrient-sensing pathway regulating lifespan. Exposing longitudinally aging yeast cells to GMP pathway inhibition in an age-dependent manner, we demonstrate that the lifespan extension is facilitated by slowing, rather than reversing, the aging process in cells. Using a GUK1 mutant with lower GMP-to-GDP conversion activity, we observe lifespan extension, suggesting that reduced GDP level by itself can also extend yeast lifespan. These findings elucidate the involvement of nucleotide metabolism in the aging process. The existence of clinically-approved GMP pathway inhibitors elicits the potential of a new class of therapeutics for aging-related disorders.

Stochastic modeling of aging cells reveals how damage accumulation, repair, and cell-division asymmetry affect clonal senescence and population fitness

BACKGROUND

Asymmetry during cellular division, both in the uneven partitioning of damaged cellular components and of cell volume, is a cell biological phenomenon experienced by many unicellular organisms. Previous work based on a deterministic model claimed that such asymmetry in the partitioning of cell volume and of aging-associated damage confers a fitness benefit in avoiding clonal senescence, primarily by diversifying the cellular population. However, clonal populations of unicellular organisms are already naturally diversified due to the inherent stochasticity of biological processes.

RESULTS

Applying a model of aging cells that accounts for natural cell-to-cell variations across a broad range of parameter values, here we show that the parameters directly controlling the accumulation and repair of damage are the most important factors affecting fitness and clonal senescence, while the effects of both segregation of damaged components and division asymmetry are frequently minimal and generally context-dependent.

CONCLUSIONS

We conclude that damage segregation and division asymmetry, perhaps counterintuitively, are not necessarily beneficial from an evolutionary perspective.

An inexpensive microscopy system for microfluidic studies in budding yeast

Recently, microfluidic technologies have been developed to allow higher throughput collection of yeast replicative lifespan data. Adoption of these devices has been limited, in part, due to the high cost of the motorized microscopy instrumentation from mainline manufacturers. Inspired by recent development of open source microscopy hardware and software, we developed minimal-cost hardware attachments to provide long-term focus stabilization for lower-cost microscopes and open source software to manage concurrent time-lapse image acquisition from multiple microscopes. We hope that these tools will help spur the wider adoption of microfluidic technologies for the study of aging in yeast.

Measuring and modeling interventions in aging

Many dietary, pharmaceutical, and genetic interventions have been found to increase the lifespan of laboratory animals. Several are now being explored for clinical application. To understand the physiologic action and therapeutic potential of interventions in aging, researchers must build quantitative models. Do interventions delay the onset of aging? Slow it down? Merely ameliorate some of its symptoms? If interventions slow some aging mechanisms but accelerate others, can we detect or predict the systemic consequences? Statistical and analytic models provide a crucial framework in which to answer these questions and clarify the systems-level effect of molecular interventions in aging. This review provides a brief survey of approaches to modeling lifespan data and places them in the context of recent experimental work.

The Systems Biology of Single-Cell Aging

Aging is a leading cause of human morbidity and mortality, but efforts to slow or reverse its effects are hampered by an incomplete understanding of its multi-faceted origins. Systems biology, the use of quantitative and computational methods to understand complex biological systems, offers a toolkit well suited to elucidating the root cause of aging. We describe the known components of the aging network and outline innovative techniques that open new avenues of investigation to the aging research community. We propose integration of the systems biology and aging fields, identifying areas of complementarity based on existing and impending technological capabilities.

Fundamental Characteristics of Single-Cell Aging in Diploid Yeast

Single-cell-level experimentation can elucidate key biological insights about cellular aging that are masked in population-level studies. However, the extensive time requirement of tracking single cells has historically prevented their long-term longitudinal observation. Using a microfluidic device that automates microscopic monitoring of diploid Saccharomyces cerevisiae cells throughout their replicative lifespan, here we report the fundamental characteristics of single-cell aging for diploid yeast. We find that proteins with short versus long half-lives exhibit distinct dynamics as cells age and that the intercellular gene expression noise increases during aging, whereas the intracellular noise stays unchanged. A stochastic model provides quantitative mechanistic insights into the observed noise dynamics and sheds light on the age-dependent intracellular noise differences between diploid and haploid yeast. Our work elucidates how a set of canonical phenotypes dynamically change while the host cells are aging in real time, providing essential insights for a comprehensive understanding on and control of lifespan at the single-cell level.

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A microfluidic device facilitates longitudinal observation of aging diploid yeast

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Proteins with short versus long half-lives exhibit distinct dynamics as cells age

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Intercellular gene expression noise increases during replicative aging

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Unlike haploid yeast, intracellular noise is unchanged during aging in diploid yeast